Personalized medicine is built on the notion that there is an inherent contradiction of going from studies of groups of patients to advice and recommendations for an individual patient. It involves tools and statistics to help clinicians advise one patient at a time, even in contradiction to results of group studies. A great part of these tools and statistics involve the genetic profile of the patient and other information (e.g. co-morbidity, concurrent medication, allergies), which can be used to tailor diagnoses and treatments based on patients' unique characteristics.
Yet, it is generally difficult to deduce from population/group studies what will work for an individual patient. Some medication may work for some patients but not for others. A multitude of factors may account for any variation in medical effects. Examples include the type of medication, dosage, absorption rate, severity of illness, drug-drug interactions, allelic combination of a patient's genes encoding detoxification enzymes, age, nutritional status, comorbidities, etc. Given the complexity of determining the right medication for patients, health providers need a tool for providing more effective prescriptions beyond the trial and error methods.
Current predictive models supposedly predict medical outcomes for individuals based on the use of a certain medicine. For example, Langheier et al. (US Pat. Appl'n US 2006/0173663 A1) discloses an optimal intervention predictive model that selects a mathematical model and estimates parameters relating the intervention to the outcome.
However, Langheier presents a complicated and less accurate approach that relies on discovering the relationship among various factors at the group level and then applying the findings to the individual at hand. Group level relationships mask what might work for individual patients. A medication may not work for the average patient but may work for some subset of patients or for the particular patient at hand.
Another approach often used in examining the effectiveness of treatments is analysis of variance. Here, statistical significance can be tested by calculating Fischer F test statistic:F=(variance of the group means)/(mean of the within group variances)   (1)
However, this assumption does not seem reasonable in the context of personalized care as using information across patients to find what works for one patient seems antithetical to the goals of personalized care. Both the variance of mean of treatment and the mean of within group variances, are calculated from experiences of others, many of whom are not like the patient at hand and their data are irrelevant to the case at hand. The analysis is faulty not because it is mathematically or logically incorrect but because it uses data that is irrelevant to task at hand. This approach assumes that what works for the average patient will also work for the patient at hand, which contradicts the very goal of personalized medicine. Neuhauser points out that there is no such thing as an average patient; all patients differ from the average reported in the literature in some unique ways and average study results are of little guidance for individual patients.
Some statisticians may propose selecting increasingly small treatment groups, so all members of the group are essentially the same as the patient at hand. The idea is that by looking at a handful of characteristics (such as 5 or 10) of the patient, a sub-group in the population database that share all these features can be found. Therefore, calculating the variance of this subgroup will be relevant to the case at hand. But in practice, any attempt to redefine treatment groups so that it matches the patient at hand is inevitably futile as when the number of features used increases. When genetic and phenotypic information are included, the number of cases within the group will go down, often to one or no case.
A number of scientists have even tried to resolve this problem by suggesting experimental studies of N of 1. One approach is to use sequential analysis. In this approach, one person may be observed over time and treatment may be modified (typically in small steps) until a statistically significant treatment is found. But the problems with statistical approaches to personalized medicine is not limited to restrictions to studies of N of 1. A more fundamental approach is the utility of statistical significance and mathematical modeling of causes of improvement.
When it comes to personalized medicine, it is important to change current methods so that it reflects the new constraints that would work for one patient. Hence, what is needed is a simplified and accurate prediction model that does not rely on selecting various models to foresee a potential medical outcome. Also, what is needed is a strategy that detects whether a particular medication works for the patient at hand—independent of whether it works for others or for an average patient.